1. 1 Eva Sørensen University College London Optimal economic design and operation of single and multi-column chromatographic processes

2. 2 Motivation 1 OR

3. 3 Motivation 2 A mixture with many unknowns Chromatogram

4. 4 Outline Single vs multicolumn processes Single column modelling: Systematic approach for model selection and model parameter estimation  Hydrophobic interaction chromatography (HIC) Multi-column modelling  Dynamic and cyclic steady state (CSS) models Optimal configuration decision: Process selection approach (Economic optimisation)  Case study Concluding remarks

5. 5 Modelling Single column  column model Single column with recycling – column model + recycling port Simulated moving bed (SMB)/Varicol – column models + nodal models + complex switching action

6. 6 SMB Operation SMB process operation  continuous, synchronous switching action of flow rates A number of cycles before steady state D R F E Mobile phase

7. 7 1 st switching period 40 th switching period 2 nd switching period 8 th switching period Problem for optimisation Dynamic SMB models

8. 8 Dynamic SMB models contd. CSS Cycle model (e.g. Nilchan and Pantelides, 1998): C i,z (j, t = 0) = C i,z (j, t = T cycle ) q i,z (j, t = 0) = q i,z (j, t = T cycle ) D R F E Mobile phase CSS Switch model (e.g. Kloppenburg and Gilles, 1999): C i,z (j, t = 0) = C i,z (j + 1, t = T switch ) q i,z (j, t = 0) = q i,z (j + 1, t = T switch ) Spatial and temporal discretisation Continuous Steady- State (CSS) models give the SMB elution profiles at steady state conditions directly

9. 9 SMB Models CSS Switch predictions are closer to the dynamic model gPROMS (PSE, 2005)

10. 10 Process Selection Approach OR

11. 11 START Separation specification I II Develop NO Is column data available? Enter model YES HOW?

12. 12 Model Selection Approach

13. 13 Modelling Chromatography General Rate (GR) Model Equilibrium-dispersive (ED) Model Comprehensive model which takes into account mass transfer resistance, diffusion and dispersion Efficient model which lumps all effects due to band broadening into a single coefficient No clear guidelines for model selection  process/conditions  purpose Given experimental data  model parameters?  model type?

14. 14 Model selection approach Common model parameters Distinct model parameters Model selection Given type of chromatography Identification of model parameters Estimation of uncertain parameters

15. 15 Model selection approach Common model parameters Distinct model parameters Model selection Given type of chromatography Identification of model parameters Estimation of uncertain parameters C Feed ?

16. 16 Calculating C Feed

17. 17 Calculating C Feed contd. Number of peaks on chromatogram, N NP Establish type of separation and characteristic property of component associated with it

18. 18 Calculating C Feed contd. Total number of components, N T Define confidence ratio, R C Define number of components for simulation, N C N C = N T - N R - N S NRNR NTNT N T - N R NSNS N C = N T - N R - N s

19. 19 Calculating C Feed contd. Define N C = N T - N R - N S Define pseudo-components N C ’ Determine order of elution No Yes Redefine N R, N S or N C ’ Time B C A D C Feed from area under peak

20. 20 The approach Common model parameters Distinct model parameters Model selection Given type of chromatography Identification of model parameters Estimation of uncertain parameters

21. 21 Uncertain parameters Isotherms:

22. 22 Model parameter estimation:

23. 23 Parameter estimation contd. Model with Parameter Estimator  estimated parameters

24. 24 The Good The Bad The Ugly Case studies

25. 25 The Bad Purification of alcohol dehydrogenase (ADH) from a yeast homogenate using hydrophobic interaction chromatography (HIC)  Step elution with 2 different buffers  10 column volumes (CV) was loaded to column at 2ml/min  Chromatograms obtained only display the total protein concentration and ADH concentration

26. 26 Number of peaks on chromatogram, N NP = 3 HIC separation; using charge of protein N T approximately 125 R C = 2, N C = 8 Define pseudo-components N C ’= 5 Determine order of elution No Yes Experimental data from Rukia Khanom, UCL (2003) The Bad contd.

27. 27 ADH Total protein

28. 28 The Bad : Which model? For full details on diagrams, see Ngiam, UCL (2002) Maximum purification factor diagram GR better prediction, especially for purity Both predict total protein concentration well GR model better for predicting ADH

29. 29 Process Selection Approach contd.

30. 30 Process selection approach: START Is base case able to meet production? Net present value (NPV) analysis Process selection END Separation specification I II III IV V VI Develop NO Scale up NO Is column data available? Enter model YES Optimisation YES gPROMS (PSE, 2005) DONE

31. 31 Details of the approach I Separation specification  Step 1 : Annual production amount  Step 2 : Annual number of operating hours  Step 3 : Actual number of operating hours (minus start-up, maintenance etc.) II Data availability  Yes : Enter model  No : Develop model

32. 32 Details of the approach contd. III (Scale-up) Does base case meet production?  Yes : proceed to optimise  No : estimate scale factor to modify diameter and flow rates only Scaled-up flow rate = Base case flow rate × Scale up factor 2 Scaled-up diameter = Base case diameter × Scale up factor (Sofer and Hagel, 1997)

33. 33 Details of the approach contd. IV Optimisation – decision variables Single column Single column with recycle SMB processVaricol process L D C Q Desorbent L D C Q Desorbent N cycles L D C Q Desorbent Q Extract / Q Raffinate Q Recycle T switch L D C Q Desorbent Q Extract / Q Raffinate Q Recycle T switch (subint’s)

34. 34 Details of the approach contd. V Economic appraisal  Estimation of capital costs  Net present value (NPV) analysis over n years VI Process selection  Based on discounted cash flow (DCF) diagram

35. 35 Case study I Separation specification Step 1  Minimum 2000 kg (components A and B) Step 2  8000 hours Step 3  Start-up/shutdown/maintenance time: 20% of production time

36. 36 Case study contd. II Availability of data Separation data for single column without recycle:

37. 37 Case study contd. Process Base case annual production Scale up factor Single column4.80 kg21 Single column with recycle 2.88 kg37.8 SMB Varicol 47.76 kg6.5 III Scale up

38. 38 IV Optimisation functions Objective functions 1. Minimum production costs: Min Φ (C total ) C total = C op + C el + C ads + C waste 2. Maximum productivity: Max Φ (P annual ) P annual = S income – C total – C raw Constraints  Minimum purity: Pu i, min < Pu i < 1  Minimum yield: Y i, min < Y i < 1  Bounded ΔP: ΔP j, min < ΔP j < ΔP j, max

39. 39 IV Optimisation 1 Minimise total production costs US $SingleRecycleSMBVaricol C total 536,000 268,000309,000 P annual (∙10 6 ) 3.00 5.375.36 Note: single column with recycle – only 1 cycle, i.e. single column

40. 40 IV Optimisation 2 Maximise annual profit US $SingleRecycleSMBVaricol C total 607,000 278,000296,000 P annual (∙10 6 ) 5.02 5.435.38 Note: single column with recycle – only 1 cycle, i.e. single column

41. 41 C total = $ 0.536 ·10 6 P annual = $ 5.02 ·10 6 Single column L = 100 cmD c =19.45 cm Q desorbent = 5.45 ml/s Y A = 0.80, Y B = 0.98 P annual = $ 3.00 ·10 6 L = 100 cmD c =22.34 cm Q desorbent = 6.59 ml/s Y A = 0.994, Y B = 0.997 C total = $ 0.607 ·10 6

42. 42 SMB column D R F E L = 20cm D c = 8.43cm T switch = 234s C total = $ 0.268 ·10 6 P annual = $ 5.37 ·10 6 Q recycle = 2.64 ml/s Q Desorbent = 1.23 ml/s Q Extract = 1.10 ml/s D R F E L = 29.57cm D c = 7.03cm T switch = 200s C total = $ 0.278 ·10 6 P annual = $ 5.43 ·10 6 Q recycle = 3.10 ml/s Q Desorbent = 1.75 ml/s Q Extract = 1.51 ml/s

43. 43 Varicol column D R F E L = 35.43cm D c = 7.86cm T switch = 87s C total = $ 0.309 ·10 6 P annual = $ 5.36 ·10 6 Q recycle = 2.82 ml/s Q Desorbent = 1.06 ml/s Q Extract = 1.01 ml/s D R F E L = 22.46cm D c = 8.95cm T switch = 54.5s C total = $ 0.296 ·10 6 P annual = $ 5.38 ·10 6 Q recycle = 3.50 ml/s Q Desorbent = 1.72 ml/s Q Extract = 1.45 ml/s

44. 44 V Economical appraisal Capital costs estimation (based on equipment-delivered costs) ProcessEstimated cost US $ Single column Single column with recycle 754,000 SMB process Varicol process 1,630,000

45. 45 VI Process selection DCF diagram over 15 years

46. 46 Case Study Summary The single column should be operated without recycling Minimising production costs does not give best overall profit The DCF for multi-column processes surpasses the single column after 4 years The DCF for SMB surpasses Varicol after 4 years Note:  SMB and Varicol limited to 8 columns  Varicol limited to 4 sub-intervals per switch

47. 47 Concluding Remarks An approach for model selection based on limited experimental data  Allows determination of best model for description of separation system An approach for process selection based on overall economics  Allows determination of best process alternative for minimum costs or overall profitability  Company specific costing can easily be included

48. 48 Optimal Design and Operation of Separation Processes

49. 49 Reactive separations Optimal design and operation Separation problem Hybrid processes Other processes ? Membrane separation (Batch) distillation Chromatographic separation Configuration Design Operation Control

50. 50 Optimal design and operation Separation problem Technique Configuration Design Operation Control

51. 51 Value-added processing of essential oils Isolated components of essential oils are starting points for perfumery materials and pharmaceuticals (e.g. Citronellal and Geraniol – from citronella oil) Enrich the essential oils in some components while reducing the amounts of others (e.g. orange oil without the lighter terpenes) Fractionation and rectification performed in  Batch distillation columns  More recently: Supercritical fluid (CO 2 ) extraction units Fractionation and rectification of essential oils

52. 52 iCPSE Objectives To advance knowledge in the area of Process Systems Engineering  To promote and facilitate the widespread adoption of systems engineering methodologies  To influence National, EU and International policy and standards To educate graduate students to the highest international level To offer world class knowledge transfer services to industry  To undertake complete lifecycle of research and development: from proof of concept to commercialisation  To address and support short, medium and long term industrial research needs on an industry-wide and company-specific manner